Image_caption_generator.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/its-rahul-cloud/MachineLearning/blob/main/Image_caption_generator.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H6lYWBViJByX",
        "outputId": "22f7d504-48ce-4300-82ff-74cdf4d09c94"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.7/dist-packages (1.5.12)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.23.0)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from kaggle) (2022.9.24)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.15.0)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.7/dist-packages (from kaggle) (6.1.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from kaggle) (4.64.1)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (2.10)\n",
            "Downloading flickr8k.zip to /content\n",
            "100% 2.12G/2.13G [00:35<00:00, 60.6MB/s]\n",
            "100% 2.13G/2.13G [00:35<00:00, 65.2MB/s]\n"
          ]
        }
      ],
      "source": [
        "! pip install kaggle\n",
        "! mkdir ~/.kaggle\n",
        "! cp kaggle.json ~/.kaggle/\n",
        "! chmod 600 ~/.kaggle/kaggle.json\n",
        "! kaggle datasets download shadabhussain/flickr8k"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D5F_apFXJ8FG"
      },
      "outputs": [],
      "source": [
        "!unzip flickr8k.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NK005eD3Nmoa"
      },
      "source": [
        "#Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "Wf60D638KBcn"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing import image\n",
        "from tensorflow.keras.applications.resnet50 import ResNet50\n",
        "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Activation\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from keras.layers import add\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "import os\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_1NeSmU0Nr0a"
      },
      "source": [
        "# Image Feature Extraction\n",
        "**extract image feature by applying Resnet50 \n",
        "\n",
        "**After executing the code, the output would be a 1x2048 vector for each image, containing the features from the images"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Try can be made using another model then Resnet50 such as inseptionv3\n"
      ],
      "metadata": {
        "id": "oSawWE8lYUfX"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XV7h9GCpKFgK",
        "outputId": "3090f72b-7cff-470a-d788-2bb7bc6cfc37"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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            "1/1 [==============================] - 0s 32ms/step\n",
            "CPU times: user 9min 46s, sys: 20 s, total: 10min 6s\n",
            "Wall time: 11min 36s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "\n",
        "#Get all filename of the image\n",
        "folder='/content/flickr_data/Flickr_Data/Images/'\n",
        "images=os.listdir(folder)\n",
        "\n",
        "#Load the CNN Architecture with Imagenet and weight\n",
        "image_model=ResNet50(weights='imagenet')\n",
        "model_new=tf.keras.Model(image_model.input,image_model.layers[-2].output)\n",
        "\n",
        "#Store image features in dictionary\n",
        "img_features=dict()\n",
        "for img in images:\n",
        "  img1=image.load_img(folder+ img,target_size=(224,224))\n",
        "  x = image.img_to_array(img1)\n",
        "  x = np.expand_dims(x,axis=0)\n",
        "  x = preprocess_input(x)\n",
        "  fea_x= model_new.predict(x)\n",
        "  fea_x1 = np.reshape(fea_x, fea_x.shape[1])\n",
        "  img_features[img] = fea_x1\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "ijHNFO4wOEB9"
      },
      "outputs": [],
      "source": [
        "from re import A\n",
        "model=tf.keras.models.Sequential([\n",
        "    \n",
        "    tf.keras.layers.Conv2D(64,(3,3),activation='relu',input_shape= (224,224,3)),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Conv2D(64,(3,3),activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(128,activation=tf.nn.relu),\n",
        "    tf.keras.layers.Dense(2048,activation=tf.nn.softmax)\n",
        "])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "5S1qxxxpcXNx"
      },
      "outputs": [],
      "source": [
        "fn=\"/content/flickr_data/Flickr_Data/Flickr_TextData/Flickr8k.token.txt\"\n",
        "f=open(fn,'r')\n",
        "capts=f.read()\n",
        "#Group all caption by filename ,for references\n",
        "captions=dict()\n",
        "i=0\n",
        "\n",
        "try:\n",
        "  for line in capts.split(\"\\n\"):\n",
        "    txt =line.split('\\t')\n",
        "    fn= txt[0].split('#')[0]\n",
        "    if fn not in captions.keys():\n",
        "        captions[fn]=[txt[1]]\n",
        "    else :\n",
        "      captions[fn].append(txt[1])\n",
        "    i+=1\n",
        "except:\n",
        "  pass"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "K0P3qru1eTiM"
      },
      "outputs": [],
      "source": [
        "def getCaptions(path):\n",
        "    \n",
        "    f = open(path, 'r')\n",
        "    capts = f.read()\n",
        "    desc = dict()\n",
        "\n",
        "    try:\n",
        "        for line in capts.split(\"\\n\"):\n",
        "            image_id = line\n",
        "            image_descs = captions[image_id]\n",
        "\n",
        "            for des in image_descs:\n",
        "                ws = des.split(\" \")\n",
        "                w = [word for word in ws if word.isalpha()]\n",
        "                des = \"startseq \" + \" \".join(w) + \" endseq\"\n",
        "                if image_id not in desc:\n",
        "                    desc[image_id] = list()\n",
        "                desc[image_id].append(des)\n",
        "    except:\n",
        "        pass\n",
        "    \n",
        "    return desc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "--e5OD7mew52"
      },
      "outputs": [],
      "source": [
        "# Split captions\n",
        "train_caps = getCaptions(\"/content/flickr_data/Flickr_Data/Flickr_TextData/Flickr_8k.trainImages.txt\")\n",
        "val_caps = getCaptions(\"/content/flickr_data/Flickr_Data/Flickr_TextData/Flickr_8k.devImages.txt\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Some objects of train  and validation captions"
      ],
      "metadata": {
        "id": "9Ugol5qeZn0h"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "GUYye9T8fFjq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eea3df73-e4e6-47f1-b582-7b9ea4bbbcf9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train_caps type:  <class 'dict'>\n",
            "First few (key, value) paris of train_caps:\n",
            "2513260012_03d33305cf.jpg ['startseq A black dog is running after a white dog in the snow endseq', 'startseq Black dog chasing brown dog through snow endseq', 'startseq Two dogs chase each other across the snowy ground endseq', 'startseq Two dogs play together in the snow endseq', 'startseq Two dogs running through a low lying body of water endseq']\n",
            "2903617548_d3e38d7f88.jpg ['startseq A little baby plays croquet endseq', 'startseq A little girl plays croquet next to a truck endseq', 'startseq The child is playing croquette by the truck endseq', 'startseq The kid is in front of a car with a put and a ball endseq', 'startseq The little boy is playing with a croquet hammer and ball beside the car endseq']\n",
            "3338291921_fe7ae0c8f8.jpg ['startseq A brown dog in the snow has something hot pink in its mouth endseq', 'startseq A brown dog in the snow holding a pink hat endseq', 'startseq A brown dog is holding a pink shirt in the snow endseq', 'startseq A dog is carrying something pink in its mouth while walking through the snow endseq', 'startseq A dog with something pink in its mouth is looking forward endseq']\n",
            "488416045_1c6d903fe0.jpg ['startseq A brown dog is running along a beach endseq', 'startseq A brown dog wearing a black collar running across the beach endseq', 'startseq A dog walks on the sand near the water endseq', 'startseq Brown dog running on the beach endseq', 'startseq The large brown dog is running on the beach by the ocean endseq']\n",
            "\n",
            "val_caps type:  <class 'dict'>\n",
            "First few (key, value) pairs of val_caps:\n",
            "2090545563_a4e66ec76b.jpg ['startseq the boy laying face down on a skateboard is being pushed along the ground by another boy endseq', 'startseq Two girls play on a skateboard in a courtyard endseq', 'startseq Two people play on a long skateboard endseq', 'startseq Two small children in red shirts playing on a skateboard endseq', 'startseq two young children on a skateboard going across a sidewalk endseq']\n",
            "3393035454_2d2370ffd4.jpg ['startseq a boy in a blue top is jumping off some rocks in the woods endseq', 'startseq A boy jumps off a tan rock endseq', 'startseq A boy jumps up in a field in the woods endseq', 'startseq A young boy jumps off a rock in the forest endseq', 'startseq Child in blue and grey shirt jumping off hill in the woods endseq']\n",
            "3695064885_a6922f06b2.jpg ['startseq A lady walking her dog through an obstacle course while other people are in the background endseq', 'startseq A small tan and white dog and trainer running an obstacle course endseq', 'startseq A woman is guiding a brown dog around an obstacle course endseq', 'startseq A woman with a hat is leading a small dog through an obstacle course endseq', 'startseq The woman is leading a dog through an obstacle course endseq']\n",
            "1679557684_50a206e4a9.jpg ['startseq a big black dog jumps in the air to catch the tennis ball in his mouth endseq', 'startseq A dog looks at another dog catching a ball in the air endseq', 'startseq A white dog is watching a black dog jump on a lawn next to a pile of large rocks endseq', 'startseq A white dog watching a black dog in the air endseq', 'startseq Two dogs playing with a tennis ball in the yard endseq']\n"
          ]
        }
      ],
      "source": [
        "print(\"train_caps type: \", type(train_caps))\n",
        "print(\"First few (key, value) paris of train_caps:\") \n",
        "for i, (k, v) in enumerate(train_caps.items()):\n",
        "    print(k, v)\n",
        "    if i>2:\n",
        "        break\n",
        "print(\"\")\n",
        "print(\"val_caps type: \", type(val_caps))\n",
        "print(\"First few (key, value) pairs of val_caps:\") \n",
        "for i, (k, v) in enumerate(val_caps.items()):\n",
        "    print(k, v)\n",
        "    if i>2:\n",
        "        break"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Tokenization\n",
        "\n",
        "providing every word in train data some kind of token to recoginize it.\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "nMrmMOBQZ7Wp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Preparing to makeword-index and index-word\n",
        "#(adding all training captions to list)\n",
        "train_captions=[]\n",
        "for key,desc_list in train_caps.items():\n",
        "  for i in range(len(desc_list)):\n",
        "    train_captions.append(desc_list[i])\n",
        "\n",
        "#  Tokenize top 5000 words in Train captions\n",
        "vocab_size=5000\n",
        "tokenizer=Tokenizer( num_words=vocab_size,\n",
        "                     oov_token=\"<unk>\",\n",
        "                     filters='!\"#$%&()*+.,-/:;=?@[\\]^_`{|}~ ')\n",
        "tokenizer.fit_on_texts(train_captions)\n",
        "word_index=tokenizer.word_index\n",
        "index_word=tokenizer.index_word "
      ],
      "metadata": {
        "id": "F4CoIfivZvd8"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tokenizer index"
      ],
      "metadata": {
        "id": "pR-qrb23czAR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "vocab_size=len(tokenizer.word_index)+1\n",
        "print(vocab_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y4reoRBjcZrp",
        "outputId": "b19c7f22-63ac-47d4-8d5c-951bd73b0bd5"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7278\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i, (k, v) in enumerate(word_index.items()):\n",
        "    print(k, v)\n",
        "    if i>2:\n",
        "        break\n",
        "print(\"\")\n",
        "print(\"index_word type: \", type(index_word))\n",
        "print(\"First few (key, value) pairs of index_word:\") \n",
        "for i, (k, v) in enumerate(index_word.items()):\n",
        "    print(k, v)\n",
        "    if i>2:\n",
        "        break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4VrGk3I8dV42",
        "outputId": "7a236bbe-1b71-4d09-f3ae-bdb2ad74827f"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<unk> 1\n",
            "a 2\n",
            "startseq 3\n",
            "endseq 4\n",
            "\n",
            "index_word type:  <class 'dict'>\n",
            "First few (key, value) pairs of index_word:\n",
            "1 <unk>\n",
            "2 a\n",
            "3 startseq\n",
            "4 endseq\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Images and image features"
      ],
      "metadata": {
        "id": "mfe6inFzeOfW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "train_fns=list(train_caps.keys())\n",
        "train_set=dict((k,img_features[k]) for k in train_fns)\n",
        "val_fns=list(val_caps.keys())\n",
        "val_set=dict((k,img_features[k])for k in val_fns)\n",
        "fn_test='/content/flickr_data/Flickr_Data/Flickr_TextData/Flickr_8k.testImages.txt'\n",
        "f=open(fn_test,'r')\n",
        "t=f.read()\n",
        "\n",
        "test_fns= t.split(\"\\n\")\n",
        "test_set=dict((k,img_features[k])for k in list(test_fns[:-1]))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Jx9yZD9eKvC",
        "outputId": "fd8db678-c539-408a-8e2b-e60d6d8d4670"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 6.2 ms, sys: 0 ns, total: 6.2 ms\n",
            "Wall time: 6.01 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create sequences of images ,input sequences and output_words for an image\n",
        "def create_sequences(tokenizer,max_length,desc_list ,photo,vocab_size):\n",
        "  X1,X2,y=list(),list(),list()\n",
        "  #walk through each discription for the image\n",
        "  for desc in desc_list:\n",
        "    #encode the sequence\n",
        "    seq= tokenizer.texts_to_sequences([desc])[0]\n",
        "    #split one sequence into mutiple  X,y pairs\n",
        "    for i in range(1,len(seq )):\n",
        "      #split into input and output pairs\n",
        "      in_seq,out_seq=seq[:i],seq[i]\n",
        "      #pad input sequence\n",
        "      in_seq = pad_sequences([in_seq],maxlen=max_length)[0]\n",
        "\n",
        "      #encode output sequence \n",
        "      out_seq=to_categorical([out_seq],num_classes=vocab_size)\n",
        "\n",
        "      #store \n",
        "      X1.append(photo)\n",
        "      X2.append(in_seq)\n",
        "      y.append(out_seq)\n",
        "    return np.array(X1) ,np.array(X2),np.array(y)\n",
        "    \n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "LiXFi-7ZeLSE"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_length=34\n"
      ],
      "metadata": {
        "id": "hzemqlD1hnYU"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#data generator ,intended to be used in a call to model.fit_genrator\n",
        "def data_generator(descriptions,photos,tokenizer,max_length,vocab_size):\n",
        "  #loop for ever over the image\n",
        "  while 1:\n",
        "    for key, desc_list in descriptions.items():\n",
        "      #retrive  the photo features\n",
        "      photo =photos[key]\n",
        "      in_img,in_seq,out_word=create_sequences(tokenizer,max_length,desc_list,photo,vocab_size)\n",
        "      yield[in_img,in_seq],out_word\n"
      ],
      "metadata": {
        "id": "RnS-59k4jPk4"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# test the data generator\n",
        "generator = data_generator(train_caps, train_set, tokenizer, max_length, vocab_size)\n",
        "inputs, outputs = next(generator)\n",
        "print(inputs[0].shape)\n",
        "print(inputs[1].shape)\n",
        "print(outputs.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E8uhHLdrkjte",
        "outputId": "0dd96258-a19d-4cc4-ef3d-ea546f5b57a7"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(13, 2048)\n",
            "(13, 34)\n",
            "(13, 1, 7278)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Word Embedding Matrix\n",
        "we are going to use GLove Word Embedding for the captions."
      ],
      "metadata": {
        "id": "xPby-KMXyEog"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget http://nlp.stanford.edu/data/glove.6B.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HcIJB0sJzS2k",
        "outputId": "dafb2ab6-89f0-49fb-9224-629591a506f2"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-10-19 10:43:53--  http://nlp.stanford.edu/data/glove.6B.zip\n",
            "Resolving nlp.stanford.edu (nlp.stanford.edu)... 171.64.67.140\n",
            "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:80... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://nlp.stanford.edu/data/glove.6B.zip [following]\n",
            "--2022-10-19 10:43:53--  https://nlp.stanford.edu/data/glove.6B.zip\n",
            "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: https://downloads.cs.stanford.edu/nlp/data/glove.6B.zip [following]\n",
            "--2022-10-19 10:43:54--  https://downloads.cs.stanford.edu/nlp/data/glove.6B.zip\n",
            "Resolving downloads.cs.stanford.edu (downloads.cs.stanford.edu)... 171.64.64.22\n",
            "Connecting to downloads.cs.stanford.edu (downloads.cs.stanford.edu)|171.64.64.22|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 862182613 (822M) [application/zip]\n",
            "Saving to: ‘glove.6B.zip’\n",
            "\n",
            "glove.6B.zip        100%[===================>] 822.24M  5.03MB/s    in 3m 10s  \n",
            "\n",
            "2022-10-19 10:47:05 (4.32 MB/s) - ‘glove.6B.zip’ saved [862182613/862182613]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip glove.6B.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cYBsrxV1zqHd",
        "outputId": "4bb3b6f0-6b5c-4f60-8fad-0b6b5b53fad9"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  glove.6B.zip\n",
            "  inflating: glove.6B.50d.txt        \n",
            "  inflating: glove.6B.100d.txt       \n",
            "  inflating: glove.6B.200d.txt       \n",
            "  inflating: glove.6B.300d.txt       \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "#load Glove vectors\n",
        "embeddings_index={}\n",
        "f=open('/content/glove.6B.200d.txt',encoding='utf-8')\n",
        "for line in f:\n",
        "  values=line.split()\n",
        "  word= values[0]\n",
        "  coefs= np.asarray(values[1:],dtype='float32')\n",
        "  embeddings_index[word]=coefs\n",
        "f.close()\n",
        "\n",
        "\n",
        "print('Found %s word vectors.'% len (embeddings_index))\n",
        "# Get 200 -dim dense vector for each   of the 10000 words in out vocubulary\n",
        "vocab_size=len(word_index)+1\n",
        "embedding_dim= 200\n",
        "embedding_matrix=np.zeros((vocab_size,embedding_dim))\n",
        "\n",
        "for word ,i in word_index.items():\n",
        "\n",
        "  embedding_vector=embeddings_index.get(word)\n",
        "  if embedding_vector is not None:\n",
        "    embedding_matrix[i]=embedding_vector\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q1HtdVwVkt1N",
        "outputId": "1a1a0245-9f70-4cb7-f44e-8d1104a04202"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 400000 word vectors.\n",
            "CPU times: user 12.9 s, sys: 821 ms, total: 13.7 s\n",
            "Wall time: 13.5 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Architecture of model\n",
        "\n",
        "*  we are going create two model \n",
        "     * image model (Resnet50)with some exam layers\n",
        "     * language model (Embedding layers using glove 200d )\n",
        "* In last concatination of both model\n",
        "   * For the next-word prediction part, there are 2 layers of LSTM followed by a Dense and Softmax layer for classification \n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "tDAMUd8C5H_P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import Dense,Embedding,LSTM,Concatenate\n",
        "from keras.layers import Input, ConvLSTM2D, RepeatVector, Conv2D,TimeDistributed"
      ],
      "metadata": {
        "id": "Tnmb_BLtCen_"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "image_model = tf.keras.models.Sequential()\n",
        "\n",
        "image_model.add(Dense(embedding_dim, input_shape=(4096,), activation='relu'))\n",
        "image_model.add(RepeatVector(max_length))\n",
        "\n",
        "language_model = tf.keras.models.Sequential()\n",
        "\n",
        "language_model.add(Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=max_length))\n",
        "language_model.add(LSTM(256, return_sequences=True))\n",
        "language_model.add(TimeDistributed(Dense(embedding_dim)))\n",
        "\n",
        "conca = Concatenate()([image_model.output, language_model.output])\n",
        "x = LSTM(128, return_sequences=True)(conca)\n",
        "x = LSTM(512, return_sequences=False)(x)\n",
        "x = Dense(vocab_size)(x)\n",
        "out = Activation('softmax')(x)\n",
        "model_1 = Model(inputs=[image_model.input, language_model.input], outputs = out)\n",
        "\n",
        "model_1.layers[2].set_weights([embedding_matrix])\n",
        "model_1.layers[2].trainable = False\n",
        "\n",
        "model_1.compile(loss='categorical_crossentropy', optimizer = Adam(learning_rate = 0.0001), metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "D3DaHQqD4PAA"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Training"
      ],
      "metadata": {
        "id": "k1wiZuWNAwcy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "# train the model, run epochs manually and save after each epoch\n",
        "epochs = 10\n",
        "steps = len(train_caps)\n",
        "for i in range(epochs):\n",
        "    # create the data generator\n",
        "    generator = data_generator(train_caps, train_set, tokenizer, max_length, vocab_size)\n",
        "    # fit for one epoch\n",
        "    model_1.fit(generator, epochs=1, steps_per_epoch=steps, verbose=1)\n"
      ],
      "metadata": {
        "id": "tGpPyYAh-EeL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Error becasue the colab is trying to change the model in graph model"
      ],
      "metadata": {
        "id": "CoqAmevoEugm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Inference\n",
        "\n",
        "\n",
        "```\n",
        "ALgo:=\n",
        "For Each Image\n",
        "KeepAddingWords = True\n",
        "While KeepAddingWords:\n",
        "yhat = model.predict([image_features,caption_sequence_so_far])\n",
        "if yhat is Endseq:\n",
        "KeepAddingWords = False\n",
        "Else:\n",
        "append yhat to caption_sequence_so_far\n",
        "\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "tsboVkKuFBKt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# map an integer to a word\n",
        "def word_for_id(integer, tokenizer):\n",
        "    return tokenizer.index_word.get(integer)\n",
        "\n",
        "# generate a description for an image\n",
        "def generate_desc(model, tokenizer, photo, max_length):\n",
        "    # seed the generation process\n",
        "    in_text = 'startseq'\n",
        "    # iterate over the whole length of the sequence\n",
        "    for i in range(max_length):\n",
        "        # integer encode input sequence\n",
        "        sequence = tokenizer.texts_to_sequences([in_text])[0]\n",
        "        #print(\"sequence after tok: \", sequence)\n",
        "        # pad input\n",
        "        sequence = pad_sequences([sequence], maxlen=max_length)\n",
        "        # predict next word\n",
        "        if i==0:\n",
        "            photo = np.expand_dims(photo, axis=0)\n",
        "        #print(\"photo: \", photo)\n",
        "        #print(\"sequence: \", sequence)\n",
        "        yhat = model.predict([photo, sequence], verbose=0)\n",
        "        # convert probability to integer\n",
        "        yhat = np.argmax(yhat)\n",
        "        # map integer to word\n",
        "        word = word_for_id(yhat, tokenizer)\n",
        "        # stop if we cannot map the word\n",
        "        if word is None:\n",
        "            break\n",
        "        # append as input for generating the next word\n",
        "        in_text += ' ' + word\n",
        "        # stop if we predict the end of the sequence\n",
        "        if word == 'endseq':\n",
        "            break\n",
        "    return in_text"
      ],
      "metadata": {
        "id": "Brcp2dDpBsAd"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def image_to_feat_vec(imagePath):\n",
        "    img1 = image.load_img(imagePath, target_size=(224, 224))\n",
        "    x = image.img_to_array(img1)\n",
        "    x = np.expand_dims(x, axis=0)\n",
        "    x = preprocess_input(x)\n",
        "    fea_x = model_new.predict(x)\n",
        "    fea_x1 = np.reshape(fea_x , fea_x.shape[1])\n",
        "    return fea_x1\n"
      ],
      "metadata": {
        "id": "45R65HgqGXnS"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# test the predict function\n",
        "imagePath = \"/content/Flickr_Data/Flickr_Data/Images/1000268201_693b08cb0e.jpg\"\n",
        "photo = image_to_feat_vec(imagePath)\n",
        "tmpimg1 = np.expand_dims(np.array(photo), axis=0)\n",
        "print(tmpimg1.shape)\n",
        "tmpcap1 = pad_sequences([[3]], maxlen=max_length)\n",
        "print(tmpcap1.shape)\n",
        "tmpout1 = model_1.predict([tmpimg1, tmpcap1], verbose=0)\n",
        "print(tmpout1.shape)\n",
        "print(tmpout1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8oS7M41bFne2",
        "outputId": "61b0bb30-8a6b-4f50-c315-eb8da1c6c0d7"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 33ms/step\n",
            "(1, 2048)\n",
            "(1, 34)\n",
            "(1, 7278)\n",
            "[[0.00013795 0.00013762 0.0001464  ... 0.00014018 0.00014291 0.00014921]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "utiliqIDFrI3"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPpN6v4Fg6WNmpFQrb9j6x9",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}